System and method for call classification

ABSTRACT

A method for voice call analysis and classification includes interceting a voice call session between an initiating device and a recipient device. Voice call data exchanged between the initiating device and the recipient device during the voice call session is transformed into a predefined data format. The transformed voice call data is analyzed to determine one or more attributes of the intercepted voice call. One or more features associated with the intercepted voice call session are identified based on the determined one or more attributes. The intercepted voice call is classified using the identified one or more features.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit of priority under 35 U.S.C. 119(a)-(d)to a Russian Application No. 2020108161 filed on Feb. 26, 2020, which isincorporated by reference herein.

FIELD OF TECHNOLOGY

The invention relates to the field of information security, and, morespecifically, to system and method for voice call classification.

BACKGROUND

At present, practically every owner of a universal mobile device (suchas a mobile telephone, a smartphone, a tablet, and so forth) is dealingwith unwanted calls from banks, medical centers, insurance offices,beauty salons, and other companies using telemarketing in their work. Inthe United States, legislation is in effect that legally prohibitscertain telemarketer calls without the consent of the user. However, themajority of companies implicitly stipulate such a clause in theircontracts and get around the legislation.

In some cases, the companies providing telephone marketing services makeactive use of intelligent bots (robots), which in the process ofcommunication may imitate people. The robots determine the level ofirritability or interest of a person and often tailor the conversationin such a manner as to avoid a marketing tone. Intelligent bots are alsooften used by hackers to obtain confidential information of the user(such as data related to credit cards, social security numbers, and soforth).

Currently there are mobile applications which determine telephonenumbers (such as TrueCaller) that are used to deal with such nuisancecalls. In the majority of cases, incoming calls are checked against adatabase of spam numbers, which is constantly updated, and if the numberof a calling spammer or hacker is present in this database the user isnotified that the calling party has been detected in intrusivecommunication.

However, at present, it is possible to make a telephone call anonymousor to mask a telephone number with the aid of number swappingtechnology. Increasingly often, hackers and other malicious entities usethis technology to call bank customers from the numbers of creditorganizations and request the information needed to make withdrawals.

Conventional systems are well equipped to deal well with the problem ofidentifying robotized calls, but typically are not able to classify acall from various malicious entities. Moreover, in new hacking schemesthe changing of the number discredits the legal subscriber instead ofthe hacker. The blocking of such numbers, for example, may result inbanks losing the ability to get through to their customers.

Thus, there is a need to solve the problem of call classification,including hacking calls with swapped numbers.

SUMMARY

Disclosed are systems and methods for classifying voice calls byanalysis of the recording and certain attributes of the voice call, andproviding the user with information on a particular class of call bymeans of call classification.

In one aspect, a method is proposed for voice call analysis andclassification, wherein the method involves steps in which: a voice callsession between an initiating device and a recipient device isintercepted. Voice call data exchanged between the initiating device andthe recipient device during the voice call session is transformed into apredefined data format. The transformed voice call data is analyzed todetermine one or more attributes of the intercepted voice call. One ormore features associated with the intercepted voice call session areidentified based on the determined one or more attributes. Theintercepted voice call is classified using the identified one or morefeatures.

In one aspect, transforming the voice call data further includesrecording the voice call data. In one aspect, the predefined data formatincludes formatted text.

In one aspect, classification type of the intercepted voice callincludes at least one of: a regular call and unwanted call. In oneaspect, the one or more identified features includes at least one of: acall category, emotional component of the voice call data, presence ofrobotized speech and call duration.

In one aspect, the call category includes at least one of:telemarketing, social survey, offer of services, fraud.

In one aspect, the call category is determined using one or more callcategorization attributes based on the recording. In one aspect, thecall categorization attributes include at least one of: words, n-grams,word-embedding, bag-of-words.

In one aspect, the emotional component of the intercepted voice call ispositive or negative based on at least one of: (i) rules in which textis broken up into words or sequences of words having a previouslyassigned positive or negative evaluation, (ii) glossaries in which thenumber of positive and negative words from a previously compiledglossary are counted, (iii) machine learning.

In one aspect, the presence of the robotized speech is determined usinga set of previously selected strings of phonemes.

In one aspect, classifying the intercepted voice call further includesusing a trained classification model based on a set of previouslyselected voice calls having features.

In one aspect, the trained classification model is based on at least oneof: naive Bayesian classifier, logistic regression, MRF (Markov RandomField) classifier, SVM (support vector machine), k-nearest neighbor,and/or decision tree.

In one aspect, the method further includes sending a notification to therecipient device. In one aspect, the notification indicates aclassification category of the intercepted voice call.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated into and constitute apart of this specification, illustrate one or more example aspects ofthe present disclosure and, together with the detailed description,serve to explain their principles and implementations.

FIG. 1 shows a block diagram of a communication network, in accordancewith aspects of the present disclosure.

FIG. 2 shows a block diagram of an intelligent communication network, inaccordance with aspects of the present disclosure.

FIG. 3 illustrates a call classification system, in accordance withaspects of the present disclosure.

FIG. 4 illustrates a method for analyzing and classifying a voice call,in accordance with aspects of the present disclosure.

FIG. 5 shows an example of a general-purpose computer system.

DETAILED DESCRIPTION

Exemplary aspects are described herein in the context of a system,method, and computer program product for voice call classification.Those of ordinary skill in the art will realize that the followingdescription is illustrative only and is not intended to be in any waylimiting. Other aspects will readily suggest themselves to those skilledin the art having the benefit of this disclosure. Reference will now bemade in detail to implementations of the example aspects as illustratedin the accompanying drawings. The same reference indicators will be usedto the extent possible throughout the drawings and the followingdescription to refer to the same or like items.

FIG. 1 shows a block diagram of a communication network. Thecommunication network 100 may include an initiating device 101, arecipient device 102, a switching node 110, and media data 111. Thecommunication network 100 provides multiple physical communicationchannels and at least one switching node 110. The switching node 110provides services for transmission of media data 111 (such as voice calldata) between the initiating device 101 and the recipient device 102.Examples of communication networks 100 may include, but are not limitedto: a computer network; a telephone network, and/or a cellularcommunication network.

The term “device” (such as initiating device and recipient device)refers to equipment which transforms user information into media data111 (such as voice call data) for transmission by communication channelsand which performs the back transformation.

The term “call-up” refers to an attempt to establish a connection withthe recipient device 102, initiated by the initiating device 101.

The term “call” refers to the process of transmitting media data 111between the initiating device 101 and the recipient device 102 within anestablished voice call session.

The media data 111 may comprise: a media file; and/or streaming data(media data sent in streaming mode).

For example, the initiating device 101 and the recipient device 102 maybe mobile telephones of subscribers of a cellular communication network,where the term “call” refers to an ordinary voice call.

FIG. 2 shows a block diagram of an intelligent network, in accordancewith aspects of the present disclosure. An example of an intelligentcommunication network 200 may include a switching node 110, a serviceswitching point 220, a service control point 230, and a branching point240. The intelligent network 200, by modernizing at least one switchingnode 110 of the communication network 100, may facilitate additionalcommunication services. It should be noted that the architecture of theintelligent network 200 is independent of the type of switching network100.

In this example, the switching node 110 retains the functions forproviding the basic services, but it may also be equipped with a serviceswitching point 220. The service switching point 220 may be configuredto provide for the initiatalizion of various call handling algorithms,may carry out instructions arriving from the service control point 230,and may monitor the process of call handling of the intelligent network200.

The service control point 230 may facilitate services in the entireintelligent network 200, providing protocols for interaction between itselements.

In one variant aspect, the intelligent network 200 may contain abranching point 240 that may be configured to provide for thetransmission of a copy of the media data 111 to other auxiliary elementsof the intelligent network 200 (for example, a speech recognitiondevice), making the services of the intelligent network 200 moreconvenient to the user.

The services provided by intelligent network 200 may include but are notlimited to: a toll-free call service, providing directory informationfree of charge; televoting, providing voting capabilities by calling upa particular number; and/or providing communications by prepaid cards.

FIG. 3 illustrates a call classification system, in accordance withaspects of the present disclosure. The call classification system 300may include a communication network 100, an initiating device 101, arecipient device 102, a call handling engine 310, media data 111 (suchas voice call data), call attributes 312, a recording unit 320, a callrecording 321, a classification unit 330, a recognition module 331, ananalysis module 332, a classification module 333, a security unit 335, aclassification model 340, and a training unit 350.

The call handling engine 310 may be configured to intercept a voice callfrom the initiating device 101 to the recipient device 102 in thecommunication network 100, to determine the characteristics of the call312. The call handling engine 310 may be further configured to send themedia data 111 to the recording unit 320. The characteristics of thecall determined at this stage may include, but are not limited to:identifier of the initiating device 101, identifier of the recipientdevice 102, a timestamp indicating time of the intercepted call, statusof the recipient device 102.

In one aspect, the communication network 100 may comprise an intelligentnetwork 200 (shown in FIG. 2), utilizing resources of public telephonenetworks.

For example, the initiating device 101 may initiate a voice call. Afterreceiving the voice call, the service switching point 220 of theswitching node 110 may determine that the connection requires switchingwith the service control point 230. The switching node 110 may establishthe corresponding voice call session and may send a signal containingthe characteristics of the call 312.

The call handling engine 310 may be configured to intercept thecharacteristics of the call 312 and to notify the switching node 110 asto the need for routing the intercepted voice call through the branchingpoint 240. The branching point 240 may establish a connection using twodifferent channels: a first channel to the recipient device 102 and asecond channel to the recording unit 320.

After the connection has been established, the voice call data 111 fromeach of the respective devices are sent through the branching point 240,where the data may be duplicated. In other words, one data stream may bedirected to the receiving party, another data stream may be directed tothe recording unit 320. Such duplication may continue until the voicecall is completed.

In one aspect, the switching node 110 may not be able to establish aconnection with the recipient device 102, at least because: therecipient device 102 responds with a “busy” signal; the recipient device102 is unavailable; an error has occurred in establishing theconnection; and/or the recipient device 102 responds with a refusal toreceive the connection.

In these scenarios, the call handling engine 310 may send a signal tothe switching node 110 indicating the need to route the interceptedvoice call to the recording unit 320.

The recording unit 320 may be configured to record the media data 111being sent within an established voice call session during a call and todirect the produced recording of the call 321 to the classification unit330. The recording of the call 321 may comprise a media file, forexample.

In one aspect, the recording unit 320 may produce a recording of thevoice call data 111 in a plurality of fragments. The recording unit 320may send a fragment recording of the voice call 321 to theclassification unit 330 prior to the actual completion of the call,thusly making it possible to determine its classification in advance.

As shown in FIG. 3, the classification unit 330 may include arecognition module 331, an analysis module 332, and a classificationmodule 333.

The recognition module 331 may be configured to transform the recordingof the call 321 received from the recording unit 320 into a predefinedformat suitable for analysis, such as formatted text.

In one aspect, the recognition module 331 may divide up the media fileinto a plurality of fragments. Each fragment of the media file may besubjected to a number of transformations, as a result of whichcoefficients are obtained which describe frequency characteristics ofthe corresponding fragment. On the basis of this data, the recognitionmodule 331 may determine with a certain probability which phoneme thecorresponding fragment belongs to.

The recognition module 331 may also be additionally trained using a setof previously selected texts to recognize strings of probable phonemes.If necessary, the recognition module 331 may reconstruct unrecognizedwords by meaning, based on the context and available statistics. Thedata obtained in the course of the recognition of phonemes and therecognition of strings of phonemes may be combined and the recognitionmodule 331 may determine the most likely sequence of words.

For example, for two equally probable phonemes “m” and “t” in the word“make”, the recognition module 331 will conclude that it is more likelythat the phoneme “m” is used in the word, since during its training itoften encountered the sequence “make money”, and more seldom “takemoney”.

In one variant aspect, in the concluding phase, the recognition module331 may transform numerals into numbers and certain punctuation marks(such as hyphens) may be put in place. This transformed text is thefinal result of the recognition, which may be sent to the analysismodule 332 by the recognition module 331.

In an aspect, the recognition module 331 may be further trained using aset of previously selected strings of phonemes to recognize robotizedspeech in a call recording 321.

In an aspect, the recognition module 331 may identify the features ofthe call 312.

The identified call features may include, but are not limited to: a callcategory, emotional component of the voice call data, presence ofrobotized speech and call duration.

The analysis module 332 may be configured to pronounce a verdict as towhether a call recording 321 pertains to at least one of the categoriesbased on an analysis of the content of the call recording 321 processedby the recognition module 331.

The categories of the call recording 321 may include, but are notlimited to: fraud; spam; offer of services; and/or regular call.

In an aspect, the analysis module 332 may transform the call recording321 processed by the recognition module 331 into a set of attributessuitable for categorization. The attributes may include, but are notlimited to: words; a sequence of words (n-grams); a vectorialrepresentation of words (word embedding); and/or a multiset of words notcounting grammar or order (bag-of-words).

Next, using a machine learning algorithm, the analysis module 332 maydetermine a type of the call recording 321 processed by the recognitionmodule 331. The machine learning classification algorithm may include,but is not limited to: Bayesian classifiers (naive Bayesian classifier);logistic regression; MRF classifier; the method of support vectors (SVM,support vector machine); nearest neighbor methods (k-nearest neighbor);and/or decision tree.

For example, the machine learning algorithm utilized by the analysismodule 332 may be trained with a large number of fraudulent callrecordings, where hackers under a variety of pretexts (checking data,financial transactions) ask for certain personal data, such as bankaccount numbers or passwords. Each fraudulent call recording may berepresented as a set of attributes. As a non-limiting example, thephrase “send password from SMS”, present in the call recording 321processed by the recognition module 331 in the form of one of theattributes, may allow the analysis module 332 to assign the callrecording 321 processed by the recognition module 331 to theclassification type of “fraud”.

In one aspect, the analysis module 332 may utilize the plurality ofattributes to make an emotional assessment of the call recording 321.Such analysis enables the analysis module 332 to determine whether theemotional content of the intercepted voice call was one of thefollowing: positive; negative; and/or neutral.

The analysis module 332 may be configured to determine the emotionalcontent of the intercepted voice call using one of the followingmethods:

-   -   based on rules, in which text is broken up into words or        sequences of words having a previously assigned positive or        negative evaluation;    -   based on glossaries, in which the number of positive and        negative words from a previously compiled glossary are counted;        such glossaries may include the particles “not” or “non”;    -   based on machine learning; and/or    -   based on a hybrid method involving the use of some or all of the        classifiers in a particular sequence.

The classification module 333 may be configured to provide a verdict asto whether the call belongs to at least one of the types of voice callsby using a trained classification model 340. The classificationcategories of voice calls may include, but are not limited to: unwantedcall and/or regular call.

In one aspect, the characteristics of a call pertaining to an alreadyknown class of voice calls may be first collected. On the basis of thecollected training data, the classification model 340 may be trained sothat voice calls having similar characteristics can be classified bythat classification model 340 with an improved accuracy.

The classification model 340 may be trained using at least the followingtraining data: the category of the call recording (such astelemarketing, social survey, offer of services, fraud and the like);the emotional components of the voice call data (positive, negative,neutral); presence of the robotized speech in the call recording and/orthe duration of the call.

For example, if the analysis module 332 has classified a call recording321 of the intercepted voice call as offering services and hasdetermined the emotional content of the call as being negative, theclassification module 333 may classify the intercepted voice call asunwanted, and pertaining to spam.

The classification algorithm employed by the classification module 333may include at least one of the following:

-   -   Bayesian classifiers (naive Bayesian classifier);    -   logistic regression;    -   MRF classifier;    -   the method of support vectors (SVM, support vector machine);    -   nearest neighbor methods (k-nearest neighbor); and/or    -   decision tree.

The security unit 335 may be configured to inform the user of therecipient device 102 as to a certain class of the voice call via anotification message. The notification message may include, but is notlimited to: an SMS message; a local notification (push notification);and/or a pop-up window.

In one aspect, the notification message sent to the recipient device 102may include the recording 321 of the voice call data processed by therecognition module 331.

For example, if during the call-up the switching node 110 was unable toestablish a connection with the recipient device 102, the classificationunit 330 may determine the class of the voice call based on therecording of media data (such as voice call data) 111 sent by theinitiating device 101. The security unit 335 may be configured to informthe user of the recipient device 102 as to the determined class of themissed voice call with a local notification, for example.

In an aspect, the security unit 335 may send a command to the switchingnode 110 in advance to terminate a call-up if the type of the call hasbeen determined at least as fraud.

The training unit 350 may be configured to train the classificationmodel 340 on the basis of new training data.

For example, if the decision of the classification model 340 has provedto be wrong, the user of the recipient device 102 may redefine the typeof the corresponding voice call. In other words, the training unit 350may train the classification model 340, based on the feedback of theuser of the recipient device 102, so that the probability of a correctdetermination of the call class is heightened during further use of theclassification model 340.

FIG. 4 illustrates a method for analyzing and classifying a voice call,in accordance with aspects of the present disclosure.

At step 410, the call handling engine 310 may intercept a voice callfrom the initiating device 101 to the recipient device 102 in thecommunication network 100.

At step 420, the recognition module 331 may transform the recording ofthe call 321 received from the recording unit 320 into a predefinedformat suitable for analysis, such as formatted text.

At step 430, the call handling engine 310 may be used to determine theattributes of the intercepted voice call 312. In one aspect, the callcategorization attributes may include at least one of: words, n-grams,word-embedding, bag-of-words.

At step 440, the analysis module 332 may transform the call recording321 processed by the recognition module 331 into a set of featuressuitable for classification. As noted above, the features may include,but are not limited to: a call category, emotional component of thevoice call data, presence of robotized speech and call duration.

At step 450, the classification module 333 may be configured to providea verdict as to whether the call belongs to at least one of theclassification categories of voice calls by using a trainedclassification model 340.

The classification categories of voice calls may include, but are notlimited to: fraud; spam; telemarketing; unwanted call; and/or regularcall.

In addition, in an aspect, at step 460 the training unit 350 may be usedto train the classification model 340 so that the accuracy of theclassification is increased in the next iteration.

In addition, in an aspect, at step 470 the security unit 335 may be usedto provide the user of the recipient device 102 with information aboutthe call class which has been determined.

FIG. 5 is a block diagram illustrating a computer system 20 on whichaspects of systems and methods for call classification may beimplemented in accordance with an exemplary aspect. The computer system20 may represent a call classification system of FIG. 3 and can be inthe form of multiple computing devices, or in the form of a singlecomputing device, for example, a desktop computer, a notebook computer,a laptop computer, a mobile computing device, a smart phone, a tabletcomputer, a server, a mainframe, an embedded device, and other forms ofcomputing devices.

As shown, the computer system 20 includes a central processing unit(CPU) 21, a system memory 22, and a system bus 23 connecting the varioussystem components, including the memory associated with the centralprocessing unit 21. The system bus 23 may comprise a bus memory or busmemory controller, a peripheral bus, and a local bus that is able tointeract with any other bus architecture. Examples of the buses mayinclude PCI, ISA, PCI-Express, HyperTransport™, InfiniBand™, Serial ATA,I2C, and other suitable interconnects. The central processing unit 21(also referred to as a processor) can include a single or multiple setsof processors having single or multiple cores. The processor 21 mayexecute one or more computer-executable code implementing the techniquesof the present disclosure. The system memory 22 may be any memory forstoring data used herein and/or computer programs that are executable bythe processor 21. The system memory 22 may include volatile memory suchas a random access memory (RAM) 25 and non-volatile memory such as aread only memory (ROM) 24, flash memory, etc., or any combinationthereof. The basic input/output system (BIOS) 26 may store the basicprocedures for transfer of information between elements of the computersystem 20, such as those at the time of loading the operating systemwith the use of the ROM 24.

The computer system 20 may include one or more storage devices such asone or more removable storage devices 27, one or more non-removablestorage devices 28, or a combination thereof. The one or more removablestorage devices 27 and non-removable storage devices 28 are connected tothe system bus 23 via a storage interface 32. In an aspect, the storagedevices and the corresponding computer-readable storage media arepower-independent modules for the storage of computer instructions, datastructures, program modules, and other data of the computer system 20.The system memory 22, removable storage devices 27, and non-removablestorage devices 28 may use a variety of computer-readable storage media.Examples of computer-readable storage media include machine memory suchas cache, SRAM, DRAM, zero capacitor RAM, twin transistor RAM, eDRAM,EDO RAM, DDR RAM, EEPROM, NRAM, RRAM, SONOS, PRAM; flash memory or othermemory technology such as in solid state drives (SSDs) or flash drives;magnetic cassettes, magnetic tape, and magnetic disk storage such as inhard disk drives or floppy disks; optical storage such as in compactdisks (CD-ROM) or digital versatile disks (DVDs); and any other mediumwhich may be used to store the desired data and which can be accessed bythe computer system 20.

The system memory 22, removable storage devices 27, and non-removablestorage devices 28 of the computer system 20 may be used to store anoperating system 35, additional program applications 37, other programmodules 38, and program data 39. The computer system 20 may include aperipheral interface 46 for communicating data from input devices 40,such as a keyboard, mouse, stylus, game controller, voice input device,touch input device, or other peripheral devices, such as a printer orscanner via one or more I/O ports, such as a serial port, a parallelport, a universal serial bus (USB), or other peripheral interface. Adisplay device 47 such as one or more monitors, projectors, orintegrated display, may also be connected to the system bus 23 across anoutput interface 48, such as a video adapter. In addition to the displaydevices 47, the computer system 20 may be equipped with other peripheraloutput devices (not shown), such as loudspeakers and other audiovisualdevices.

The computer system 20 may operate in a network environment, using anetwork connection to one or more remote computers 49. The remotecomputer (or computers) 49 may be local computer workstations or serverscomprising most or all of the aforementioned elements in describing thenature of a computer system 20. Other devices may also be present in thecomputer network, such as, but not limited to, routers, networkstations, peer devices or other network nodes. The computer system 20may include one or more network interfaces 51 or network adapters forcommunicating with the remote computers 49 via one or more networks suchas a local-area computer network (LAN) 50, a wide-area computer network(WAN), an intranet, and the Internet. Examples of the network interface51 may include an Ethernet interface, a Frame Relay interface, SONETinterface, and wireless interfaces.

Aspects of the present disclosure may be a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store program code in the form of instructions or datastructures that can be accessed by a processor of a computing device,such as the computing system 20. The computer readable storage mediummay be an electronic storage device, a magnetic storage device, anoptical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination thereof. Byway of example, such computer-readable storage medium can comprise arandom access memory (RAM), a read-only memory (ROM), EEPROM, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),flash memory, a hard disk, a portable computer diskette, a memory stick,a floppy disk, or even a mechanically encoded device such as punch-cardsor raised structures in a groove having instructions recorded thereon.As used herein, a computer readable storage medium is not to beconstrued as being transitory signals per se, such as radio waves orother freely propagating electromagnetic waves, electromagnetic wavespropagating through a waveguide or transmission media, or electricalsignals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing devices from a computer readablestorage medium or to an external computer or external storage device viaa network, for example, the Internet, a local area network, a wide areanetwork and/or a wireless network. The network may comprise coppertransmission cables, optical transmission fibers, wireless transmission,routers, firewalls, switches, gateway computers and/or edge servers. Anetwork interface in each computing device receives computer readableprogram instructions from the network and forwards the computer readableprogram instructions for storage in a computer readable storage mediumwithin the respective computing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembly instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language, and conventional procedural programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a LAN or WAN, or theconnection may be made to an external computer (for example, through theInternet). In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute the computerreadable program instructions by utilizing state information of thecomputer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

In various aspects, the systems and methods described in the presentdisclosure can be addressed in terms of modules. The term “module” asused herein refers to a real-world device, component, or arrangement ofcomponents implemented using hardware, such as by an applicationspecific integrated circuit (ASIC) or FPGA, for example, or as acombination of hardware and software, such as by a microprocessor systemand a set of instructions to implement the module's functionality, which(while being executed) transform the microprocessor system into aspecial-purpose device. A module may also be implemented as acombination of the two, with certain functions facilitated by hardwarealone, and other functions facilitated by a combination of hardware andsoftware. In certain implementations, at least a portion, and in somecases, all, of a module may be executed on the processor of a computersystem. Accordingly, each module may be realized in a variety ofsuitable configurations, and should not be limited to any particularimplementation exemplified herein.

In the interest of clarity, not all of the routine features of theaspects are disclosed herein. It would be appreciated that in thedevelopment of any actual implementation of the present disclosure,numerous implementation-specific decisions must be made in order toachieve the developer's specific goals, and these specific goals willvary for different implementations and different developers. It isunderstood that such a development effort might be complex andtime-consuming, but would nevertheless be a routine undertaking ofengineering for those of ordinary skill in the art, having the benefitof this disclosure.

Furthermore, it is to be understood that the phraseology or terminologyused herein is for the purpose of description and not of restriction,such that the terminology or phraseology of the present specification isto be interpreted by the skilled in the art in light of the teachingsand guidance presented herein, in combination with the knowledge ofthose skilled in the relevant art(s). Moreover, it is not intended forany term in the specification or claims to be ascribed an uncommon orspecial meaning unless explicitly set forth as such.

The various aspects disclosed herein encompass present and future knownequivalents to the known modules referred to herein by way ofillustration. Moreover, while aspects and applications have been shownand described, it would be apparent to those skilled in the art havingthe benefit of this disclosure that many more modifications thanmentioned above are possible without departing from the inventiveconcepts disclosed herein.

1. A method for analyzing and classifying a voice call, the methodcomprising: intercepting a voice call session between an initiatingdevice and a recipient device; transforming voice call data exchangedbetween the initiating device and the recipient device during the voicecall session into a predefined data format; analyzing the transformedvoice call data to determine one or more attributes of the interceptedvoice call; identifying one or more features associated with theintercepted voice call session based on the determined one or moreattributes; and classifying the intercepted voice call using the one ormore identified features.
 2. The method of claim 1, wherein transformingthe voice call data further comprises recording the voice call data andwherein the predefined data format comprises formatted text.
 3. Themethod of claim 2, wherein classification type of the intercepted voicecall includes at least one of: a regular call and unwanted call andwherein the one or more identified features includes at least one of: acall category, emotional component of the voice call data, presence ofrobotized speech and call duration.
 4. The method of claim 3, whereinthe call category includes at least one of: telemarketing, socialsurvey, offer of services, fraud.
 5. The method of claim 4, wherein thecall category is determined using one or more call categorizationattributes based on the recording, and wherein the call categorizationattributes include at least one of: words, n-grams, word-embedding,bag-of-words.
 6. The method of claim 3, wherein the emotional componentof the intercepted voice call is positive or negative based on at leastone of: (i) rules in which text is broken up into words or sequences ofwords having a previously assigned positive or negative evaluation, (ii)glossaries in which the number of positive and negative words from apreviously compiled glossary are counted, (iii) machine learning.
 7. Themethod of claim 3, wherein the presence of the robotized speech isdetermined using a set of previously selected strings of phonemes. 8.The method of claim 1, wherein classifying the intercepted voice callfurther comprises using a trained classification model based on a set ofpreviously selected voice calls having features.
 9. The method of claim8, wherein the trained classification model is based on at least one of:naive Bayesian classifier, logistic regression, MRF (Markov RandomField) classifier, SVM (support vector machine), k-nearest neighbor,and/or decision tree.
 10. The method of claim 1, further comprisingsending a notification to the recipient device, wherein the notificationindicates a classification category of the intercepted voice call.
 11. Asystem for analyzing and classifying a voice call, the systemcomprising: a hardware processor configured to: intercept a voice callsession between an initiating device and a recipient device; transformvoice call data exchanged between the initiating device and therecipient device during the voice call session into a predefined dataformat; analyze the transformed voice call data to determine one or moreattributes of the intercepted voice call; identify one or more featuresassociated with the intercepted voice call session based on thedetermined one or more attributes; and classify the intercepted voicecall using the one or more identified features.
 12. The system of claim11, wherein the hardware processor configured to transform the voicecall data is further configured to record the voice call data andwherein the predefined data format comprises formatted text.
 13. Thesystem of claim 12, wherein classification type of the intercepted voicecall includes at least one of: a regular call and unwanted call andwherein the one or more identified features includes at least one of: acall category, emotional component of the voice call data, presence ofrobotized speech and call duration.
 14. The system of claim 13, whereinthe call category includes at least one of: telemarketing, socialsurvey, offer of services, fraud.
 15. The system of claim 14, whereinthe call category is determined using one or more call categorizationattributes based on the recording, and wherein the call categorizationattributes include at least one of: words, n-grams, word-embedding,bag-of-words.
 16. The system of claim 13, wherein the emotionalcomponent of the intercepted voice call is positive or negative based onat least one of: (i) rules in which text is broken up into words orsequences of words having a previously assigned positive or negativeevaluation, (ii) glossaries in which the number of positive and negativewords from a previously compiled glossary are counted, (iii) machinelearning.
 15. (canceled)
 16. (canceled)
 17. The system of claim 13,wherein the presence of the robotized speech is determined using a setof previously selected strings of phonemes.
 18. The system of claim 11,wherein the hardware processor configured to classify the interceptedvoice call is further configured to use a trained classification modelbased on a set of previously selected voice calls having features. 19.The system of claim 18, wherein the trained classification model isbased on at least one of: naive Bayesian classifier, logisticregression, MRF (Markov Random Field) classifier, SVM (support vectormachine), k-nearest neighbor, and/or decision tree.
 20. A non-transitorycomputer readable medium storing thereon computer executableinstructions for analyzing and classifying a voice call, includinginstructions for: intercepting a voice call session between aninitiating device and a recipient device; transforming voice call dataexchanged between the initiating device and the recipient device duringthe voice call session into a predefined data format; analyzing thetransformed voice call data to determine one or more attributes of theintercepted voice call; identifying one or more features associated withthe intercepted voice call session based on the determined one or moreattributes; and classifying the intercepted voice call using the one ormore identified features.